Automated, optimized, intelligent data collection for cryo-EM

冷冻电镜的自动化、优化、智能数据采集

基本信息

批准号：
10649517
负责人：
Gabriel C Lander
金额：
$ 55.35万
依托单位：
SCRIPPS RESEARCH INSTITUTE, THE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-22 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10649517
关键词：
2019-nCoV Adoption Algorithms Automation Automobile Driving Buffers Bypass COVID-19 Calibration Code Communities Computer software Country Cryoelectron Microscopy Data Data Collection Data Set Databases Development Disease Electron Microscope Electron Microscopy Electron Microscopy Facility Electrons Feedback Generations Goals Hand Heterogeneity Ice Image Image Analysis Institution Intelligence Intervention Investments Learning Maintenance Manuals Methodology Methods Microscope Molecular Molecular Conformation Output Particle Size Pathologic Planets Play Plug-in Preparation Process Pythons Research Resolution Role Running Sampling Series Site Specimen Structure Thick Time Training United States National Institutes of Health Update Vaccines Virus Visualization Work application programming interface base biological systems biophysical properties cluster computing comparative computer infrastructure computer program computerized data processing convolutional neural network data acquisition data mining data quality design fighting image processing improved innovation migration neutralizing antibody next generation open source particle portability prediction algorithm preservation programs real-time images reconstruction structural biology therapeutic development tool

项目摘要

Project Summary Cryo-electron microscopy (cryo-EM) is now a widely established and indispensable method for determining the high-resolution structures of biomedically important molecules. Given that thousands of images, often acquired over the course of several days, are required to obtain such structures, automation software has played a critical role in the large-scale adoption of this method by the scientific community. In just the past five years, cryo-EM has revolutionized our understanding of entire biological systems, and in 2020 provided the first molecular descriptions of SARS-CoV-2 interaction with neutralizing antibodies. The widespread adoption of cryo-EM recently prompted the NIH to invest in three National Centers through the Transformative High Resolution Cryo- Electron Microscopy Program, providing free, high-end electron microscope access to biologists across the country. The exponential increase in the popularity of cryo-EM has led to an astonishing number of developments in sample preparation methodologies and image processing algorithms, which have improved attainable resolution of single particle reconstructions. However, comparatively little progress has been made in optimizing the quality of the cryo-EM data being collected. The pioneering software packages Leginon and Appion demonstrated the power of automated data acquisition and real-time processing (respectively), and there are now numerous programs for automated data acquisition and real-time processing. Despite advances in automation, optimally extracting the highest quality data from an EM sample still requires manual involvement of an expert electron microscopist. User intervention and expertise is necessary to run the appropriate image analyses, interpret the results, and make informed decisions on how the processed results relate to the ongoing data collection. However, even experts must content with the fact that the “best grid regions” differ drastically from sample to sample, and there are no established tools for automatically and quickly assessing the quality of the specimen across the various microenvironments of an EM grid. Given the ever-increasing incorporation of cryo-EM into labs’ research programs, it is imperative that data collection and processing be streamlined to match the growing needs of the structural community. We propose to develop a second generation Leginon/Appion software package, “Magellon”, to overcome existing bottlenecks and provide an avenue toward fully automated data acquisition that bypasses need for user input during data collection. Importantly, this software will support the computational infrastructure to enable real-time image processing results to inform on and modify the ongoing data collection regime by learning where to acquire images in regions that will yield the highest resolution structures. We will develop and incorporate new, fast image assessment routines, while also providing an application programming interface to enable the incorporation of extensions and plugins from developers in the community. Further, Magellon will enable straightforward, seamless import and export of data from its database to accommodate remote data acquisition at any of the regional or national cryo-EM centers.

项目概要冷冻电子显微镜（cryo-EM）现在是一种广泛建立的、不可或缺的方法，用于确定鉴于通常需要获取数千张图像，因此可以获取生物医学上重要分子的高分辨率结构。在几天的时间里，需要这样的结构，自动化软件发挥了关键作用在过去的五年里，冷冻电镜在科学界大规模采用这种方法中发挥了重要作用。彻底改变了我们对整个生物系统的理解，并于 2020 年提供了第一个分子 SARS-CoV-2 与中和抗体相互作用的描述冷冻电镜的广泛采用。最近促使 NIH 通过变革性高分辨率冷冻技术投资三个国家中心电子显微镜计划，为世界各地的生物学家提供免费的高端电子显微镜使用机会冷冻电镜的普及率呈指数级增长，带来了数量惊人的发展。在样品制备方法和图像处理算法方面，这些改进可实现然而，在优化方面取得的进展相对较小。领先的软件包 Leginon 和 Appion 所收集的冷冻电镜数据的质量。分别展示了自动数据采集和实时处理的强大功能，并且有尽管在技术方面取得了进步，但现在有许多用于自动数据采集和实时处理的程序。自动化，从 EM 样本中最佳地提取最高质量的数据仍然需要手动参与需要专业的电子显微镜专家的干预和专业知识才能运行适当的图像。分析、解释结果，并就处理结果与正在进行的结果的关系做出明智的决策然而，即使是专家也必须满足于“最佳网格区域”差异巨大的事实。从一个样本到另一个样本，目前还没有成熟的工具来自动、快速地评估样本的质量鉴于不断增加的结合，样本跨越了电磁网格的各种微环境。当冷冻电镜融入实验室的研究计划时，必须简化数据收集和处理，以我们建议开发第二代产品以满足结构界不断增长的需求。 Leginon/Appion 软件包“Magellon”克服了现有瓶颈并提供了一条途径重要的是，完全自动化的数据采集无需用户在数据收集期间输入。软件将支持计算基础设施，使实时图像处理结果能够告知并通过了解在何处获取区域中的图像来修改正在进行的数据收集制度，从而产生我们将开发并采用新的快速图像评估程序，同时还将提供应用程序编程接口以实现扩展和插件的合并此外，Magellon 将支持直接、无缝的数据导入和导出。从其数据库中获取任何区域或国家冷冻电镜中心的远程数据。